Emails Kaggle Data Sets.csv
1.3 GB
📦 Datasets Name: Emails Datasets
⚙ Format: CSV file
🔐 From: Kaggle
https://news.1rj.ru/str/DataPortfolio
⚙ Format: CSV file
🔐 From: Kaggle
https://news.1rj.ru/str/DataPortfolio
Best Alzheimer MRI dataset.zip
71.5 MB
📦 Datasets name: Alzheimer MRI dataset
⚙ Format: images files
🔐 From: Kaggle
⚙ Format: images files
🔐 From: Kaggle
Retinopathy-Diabetes Dataset.zip
365.1 MB
📦 Datasets name: Retinopathy & Diabetes Dataset
⚙ Format: images files
🔐 From: Kaggle
https://news.1rj.ru/str/DataPortfolio
⚙ Format: images files
🔐 From: Kaggle
https://news.1rj.ru/str/DataPortfolio
deep learning notes.pdf
19.1 MB
Deep Learning Notes
Data_Science_from_Scratch_First_Principles_with_Python_by_Joel_Grus.pdf
10.8 MB
Data Science from Scratch First Principles with Python by Joel Grus z lib
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𝟰 𝗙𝗥𝗘𝗘 𝗕𝗲𝘀𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝗮𝘃𝗮 𝗘𝗮𝘀𝗶𝗹𝘆 😍
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Level up your Java skills without getting overwhelmed
All of them are absolutely free, designed by experienced educators and top tech creators
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👍1
Top 5 Case Studies for Data Analytics: You Must Know Before Attending an Interview
1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.
2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.
3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.
4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.
5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.
Like if it helps 😄
1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.
2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.
3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.
4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.
5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.
Like if it helps 😄
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𝟯 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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Want to build your tech career without breaking the bank?💰
These 3 completely free courses are all you need to begin your journey in programming and data analysis📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3EtHnBI
Learn at your own pace, sharpen your skills, and showcase your progress on LinkedIn or your resume. Let’s dive in!✅️
👍1
How to Reach Recruiters on LinkedIn (The Smart Way!) 💼✨
Looking for a job? Want to get noticed by recruiters?
Here’s how you can professionally reach out and actually get a response 👇
✅ 1. Polish Your Profile First
Your profile is your first impression — make it count!
📌 Use a professional photo
📌 Write a clear, concise headline
📌 Highlight your key skills & experience
📌 Add a compelling “About” section
✅ 2. Search for the Right Recruiters
Use keywords like:
🔹 “Recruiter + [Company Name]”
🔹 “Talent Acquisition + [Domain/Role]”
This helps you connect with recruiters relevant to your field.
✅ 3. Send a Personal Connection Request 📨
Don’t just hit Connect — always add a note! Here's a simple format:
“Hi [Name], I admire the work your team is doing at [Company]. I’m exploring opportunities in [role/domain] and would love to connect with you. Thanks in advance!”
✅ 4. Engage Before You Message
Like their posts. Leave thoughtful comments. Show up on their radar before starting a conversation. 👀
✅ 5. Don’t Immediately Ask for a Job ❌
Build a connection first. Instead, ask: 💬 “Could you share what qualities you usually look for in [role] candidates?”
This shows initiative without pressure.
✅ 6. Follow Up Respectfully 🔁
If you don’t get a response, follow up once after a few days.
Keep it short, friendly, and respectful. Patience pays.
✅ 7. Be Active & Consistent 📣
Post updates, share your learnings, and interact with industry content.
Recruiters often notice candidates who are visible and engaged!
💡 Remember: LinkedIn is not just about job hunting – it's about building professional relationships.
Stay genuine. Stay professional. Stay consistent. 🌟
✨ You never know which message could change your career path.
Looking for a job? Want to get noticed by recruiters?
Here’s how you can professionally reach out and actually get a response 👇
✅ 1. Polish Your Profile First
Your profile is your first impression — make it count!
📌 Use a professional photo
📌 Write a clear, concise headline
📌 Highlight your key skills & experience
📌 Add a compelling “About” section
✅ 2. Search for the Right Recruiters
Use keywords like:
🔹 “Recruiter + [Company Name]”
🔹 “Talent Acquisition + [Domain/Role]”
This helps you connect with recruiters relevant to your field.
✅ 3. Send a Personal Connection Request 📨
Don’t just hit Connect — always add a note! Here's a simple format:
“Hi [Name], I admire the work your team is doing at [Company]. I’m exploring opportunities in [role/domain] and would love to connect with you. Thanks in advance!”
✅ 4. Engage Before You Message
Like their posts. Leave thoughtful comments. Show up on their radar before starting a conversation. 👀
✅ 5. Don’t Immediately Ask for a Job ❌
Build a connection first. Instead, ask: 💬 “Could you share what qualities you usually look for in [role] candidates?”
This shows initiative without pressure.
✅ 6. Follow Up Respectfully 🔁
If you don’t get a response, follow up once after a few days.
Keep it short, friendly, and respectful. Patience pays.
✅ 7. Be Active & Consistent 📣
Post updates, share your learnings, and interact with industry content.
Recruiters often notice candidates who are visible and engaged!
💡 Remember: LinkedIn is not just about job hunting – it's about building professional relationships.
Stay genuine. Stay professional. Stay consistent. 🌟
✨ You never know which message could change your career path.
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𝗔𝗜 & 𝗠𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
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Enroll Now & Get Certified 🎓
Qualcomm—a global tech giant offering completely FREE courses that you can access anytime, anywhere.
✅ 100% Free — No hidden charges, subnoscriptions, or trials
✅ Created by Industry Experts
✅ Self-paced & Online — Learn from anywhere, anytime
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3YrFTyK
Enroll Now & Get Certified 🎓
👍1
Forwarded from Generative AI
𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍
JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills.
- Software Engineering Internship
- Investment Banking Program
- Quantitative Research Internship
𝐋𝐢𝐧𝐤 👇:-
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Enroll For FREE & Get Certified 🎓
JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills.
- Software Engineering Internship
- Investment Banking Program
- Quantitative Research Internship
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4gHGofl
Enroll For FREE & Get Certified 🎓
Python Libraries for Data Science
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𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 😍
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Mercedes :- https://pdlink.in/3RPLXNM
TechM :- https://pdlink.in/4cws0oN
SE :- https://pdlink.in/42feu5D
Siemens :- https://pdlink.in/4jxhzDR
Dxc :- https://pdlink.in/4ctIeis
EY:- https://pdlink.in/4lwMQZo
Apply before the link expires 💫
📝 Ghibli Dataset
❓Original to #Ghibli art image generator and classifier dataset.
❓Original to #Ghibli art image generator and classifier dataset.
The Ghibli Art Image Dataset is a prototype designed for generating Ghibli-style images using machine learning. It includes three subsets—training, testing, and validation—each containing directories with two PNG images: one original (o.png) and one generated in Ghibli style (g.png). This dataset supports tasks like image classification and Ghibli-style image generation. As a small-scale version of a larger dataset, it provides essential resources like model code and a pre-trained Generator.pth model. The images were collected from platforms such as Meta, Google, and Instagram for research and experimentation purposes.
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